Redistribution effects of water tariffs

Nguyen Bich Ngoc, Jacques Teller 13 January 2022

## Warning: Removed 150 row(s) containing missing values (geom_path).

## Warning: Removed 150 row(s) containing missing values (geom_path).

## Warning: Removed 192 row(s) containing missing values (geom_path).

summary(df$TEH)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.2122  0.7290  1.0389  1.2432  1.5183  9.0135

sum(df$TEH > 3) * 100 / nrow(df)

## [1] 3.645833

## 3.2 by income quantiles -------

### income quantiles -------

# Correlation between water consumption and household income should use spearman?????

cor.test(df$csmptv, df$income, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$income
## t = 15.729, df = 1726, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.3121384 0.3946480
## sample estimates:
##      cor 
## 0.354082

cor.test(df$csmptv, df$income, method = "spearman")

## Warning in cor.test.default(df$csmptv, df$income, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$income
## S = 536353649, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.3763062

# Correlation between water consumption and income per equivalent adult should use spearman?????

cor.test(df$csmptv, df$inceqa, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  df$csmptv and df$inceqa
## t = 1.4269, df = 1726, p-value = 0.1538
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01285165  0.08134826
## sample estimates:
##        cor 
## 0.03432454

cor.test(df$csmptv, df$inceqa, method = "spearman")

## Warning in cor.test.default(df$csmptv, df$inceqa, method = "spearman"):
## Cannot compute exact p-value with ties

## 
##  Spearman's rank correlation rho
## 
## data:  df$csmptv and df$inceqa
## S = 803896839, p-value = 0.006707
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.06519613

## Warning: Graphs cannot be horizontally aligned unless the axis parameter
## is set. Placing graphs unaligned.

summary(df$avrprc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.200   4.595   4.690   5.000   4.850  12.549

summary(df$avrprc[df$inccat == "precarious"])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   4.222   4.605   4.734   5.152   4.915  10.592       1

summary(df$subs[df$inccat == "precarious"])

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
## -68.1035  -4.7756   0.5658  -4.0094   8.3744  27.4910        1

summary(df$mgnprc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.158   4.458   4.458   3.982   4.458   4.658

summary(df$mgnprc[df$inccat == "precarious"])

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.158   4.153   4.458   3.754   4.458   4.658       1

## 3.5. changing fixed  -----

### new cvd ------